from datetime import datetime
import pandas as pd
from pathlib import Path
import plotly
import plotly.express as px
import numpy as np
from statsmodels.tsa.api import VAR
import urllib.request
plotly.offline.init_notebook_mode()
NOW = datetime.now()
TODAY = NOW.date()
print('Aktualisiert:', NOW)
Aktualisiert: 2020-12-17 14:07:33.757557
STATE_NAMES = ['Burgenland', 'Kärnten', 'Niederösterreich',
'Oberösterreich', 'Salzburg', 'Steiermark',
'Tirol', 'Vorarlberg', 'Wien']
# TODO: Genauer recherchieren!
EVENTS = {'1. Lockdown': (np.datetime64('2020-03-20'), np.datetime64('2020-04-14'),
'red', 'inside top left'),
'1. Maskenpflicht': (np.datetime64('2020-03-30'), np.datetime64('2020-06-15'),
'yellow', 'inside bottom left'),
'2. Maskenpflicht': (np.datetime64('2020-07-24'), np.datetime64(TODAY),
'yellow', 'inside bottom left'),
'1. Soft Lockdown': (np.datetime64('2020-11-03'), np.datetime64('2020-11-17'),
'orange', 'inside top left'),
'2. Lockdown': (np.datetime64('2020-11-17'), np.datetime64('2020-12-06'),
'red', 'inside top left'),
'2. Soft Lockdown': (np.datetime64('2020-12-06'), np.datetime64(TODAY),
'orange', 'inside top left')}
def load_data(URL, date_columns):
data_file = Path(URL).name
try:
# Only download the data if we don't have it, to avoid
# excessive server access during local development
with open(data_file):
print("Using local", data_file)
except FileNotFoundError:
print("Downloading", URL)
urllib.request.urlretrieve(URL, data_file)
return pd.read_csv(data_file, sep=';', parse_dates=date_columns, infer_datetime_format=True, dayfirst=True)
raw_data = load_data("https://covid19-dashboard.ages.at/data/CovidFaelle_Timeline.csv", [0])
additional_data = load_data("https://covid19-dashboard.ages.at/data/CovidFallzahlen.csv", [0, 2])
Downloading https://covid19-dashboard.ages.at/data/CovidFaelle_Timeline.csv Downloading https://covid19-dashboard.ages.at/data/CovidFallzahlen.csv
cases = raw_data.query("Bundesland == 'Österreich'")
cases.insert(0, 'AnzahlFaelle_avg7', cases.AnzahlFaelle7Tage / 7)
time = cases.Time
tests = additional_data.query("Bundesland == 'Alle'")
tests.insert(2, 'TagesTests', np.concatenate([[np.nan], np.diff(tests.TestGesamt)]))
tests.insert(3, 'TagesTests_avg7', np.concatenate([[np.nan] * 7, (tests.TestGesamt.values[7:] - tests.TestGesamt.values[:-7])/7]))
tests.insert(0, 'Time', tests.MeldeDatum)
fig = px.line(cases, x='Time', y=["AnzahlFaelle", "AnzahlFaelle_avg7"], log_y=True, title="Fallzahlen")
fig.add_scatter(x=tests.Time, y=tests.TagesTests, name='Tests')
for name, (begin, end, color, pos) in EVENTS.items():
fig.add_vrect(x0=begin, x1=end, name=name, fillcolor=color, opacity=0.2,
annotation={'text': name}, annotation_position=pos)
fig.show()
all_data = tests.merge(cases, on='Time', how='outer')
all_data.insert(1, 'PosRate', all_data.AnzahlFaelle / all_data.TagesTests)
all_data.insert(1, 'PosRate_avg7', all_data.AnzahlFaelle_avg7 / all_data.TagesTests_avg7)
fig = px.line(all_data, x='Time', y=['PosRate', 'PosRate_avg7'], log_y=False, title="Anteil Positiver Tests")
for name, (begin, end, color, pos) in EVENTS.items():
fig.add_vrect(x0=begin, x1=end, name=name, fillcolor=color, opacity=0.2,
annotation={'text': name}, annotation_position=pos)
fig.show()
states = []
rates = []
for state_name, state_data in raw_data.groupby('Bundesland'):
x = np.log2(state_data.AnzahlFaelle7Tage)
rate = 2**np.array(np.diff(x))
rates.append(rate)
states.append(state_name)
growth = pd.DataFrame({n: r for n, r in zip(states, rates)})
fig = px.line(growth, x=time[1:], y=STATE_NAMES, title='Wachstumsrate')
fig.update_layout(yaxis=dict(range=[0.25, 4]))
fig.show()
/usr/share/miniconda/lib/python3.8/site-packages/pandas/core/series.py:726: RuntimeWarning: divide by zero encountered in log2 /usr/share/miniconda/lib/python3.8/site-packages/numpy/lib/function_base.py:1280: RuntimeWarning: invalid value encountered in subtract
model = VAR(growth[150:][STATE_NAMES])
res = model.fit(1)
res.summary()
Summary of Regression Results
==================================
Model: VAR
Method: OLS
Date: Thu, 17, Dec, 2020
Time: 14:07:37
--------------------------------------------------------------------
No. of Equations: 9.00000 BIC: -43.6663
Nobs: 143.000 HQIC: -44.7733
Log likelihood: 1519.29 FPE: 1.68665e-20
AIC: -45.5310 Det(Omega_mle): 9.17966e-21
--------------------------------------------------------------------
Results for equation Burgenland
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.454831 0.174109 2.612 0.009
L1.Burgenland 0.145900 0.084464 1.727 0.084
L1.Kärnten -0.234839 0.068183 -3.444 0.001
L1.Niederösterreich 0.125293 0.203971 0.614 0.539
L1.Oberösterreich 0.246953 0.169565 1.456 0.145
L1.Salzburg 0.176623 0.087342 2.022 0.043
L1.Steiermark 0.084110 0.122151 0.689 0.491
L1.Tirol 0.144748 0.080564 1.797 0.072
L1.Vorarlberg 0.007030 0.078251 0.090 0.928
L1.Wien -0.133140 0.165544 -0.804 0.421
======================================================================================
Results for equation Kärnten
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.577727 0.227164 2.543 0.011
L1.Burgenland 0.012443 0.110202 0.113 0.910
L1.Kärnten 0.363726 0.088960 4.089 0.000
L1.Niederösterreich 0.145963 0.266125 0.548 0.583
L1.Oberösterreich -0.215685 0.221236 -0.975 0.330
L1.Salzburg 0.190363 0.113957 1.670 0.095
L1.Steiermark 0.232964 0.159373 1.462 0.144
L1.Tirol 0.142024 0.105113 1.351 0.177
L1.Vorarlberg 0.187728 0.102096 1.839 0.066
L1.Wien -0.616592 0.215989 -2.855 0.004
======================================================================================
Results for equation Niederösterreich
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.309206 0.074239 4.165 0.000
L1.Burgenland 0.103316 0.036015 2.869 0.004
L1.Kärnten -0.024001 0.029073 -0.826 0.409
L1.Niederösterreich 0.098737 0.086972 1.135 0.256
L1.Oberösterreich 0.284444 0.072302 3.934 0.000
L1.Salzburg -0.004899 0.037242 -0.132 0.895
L1.Steiermark -0.031975 0.052084 -0.614 0.539
L1.Tirol 0.088353 0.034352 2.572 0.010
L1.Vorarlberg 0.130150 0.033366 3.901 0.000
L1.Wien 0.054450 0.070587 0.771 0.440
======================================================================================
Results for equation Oberösterreich
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.204660 0.085989 2.380 0.017
L1.Burgenland -0.005423 0.041715 -0.130 0.897
L1.Kärnten 0.021682 0.033674 0.644 0.520
L1.Niederösterreich 0.032253 0.100738 0.320 0.749
L1.Oberösterreich 0.402682 0.083745 4.808 0.000
L1.Salzburg 0.092562 0.043137 2.146 0.032
L1.Steiermark 0.190144 0.060328 3.152 0.002
L1.Tirol 0.030434 0.039789 0.765 0.444
L1.Vorarlberg 0.102400 0.038647 2.650 0.008
L1.Wien -0.071775 0.081759 -0.878 0.380
======================================================================================
Results for equation Salzburg
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.635999 0.182981 3.476 0.001
L1.Burgenland 0.077005 0.088768 0.867 0.386
L1.Kärnten 0.003952 0.071657 0.055 0.956
L1.Niederösterreich -0.059901 0.214365 -0.279 0.780
L1.Oberösterreich 0.129920 0.178206 0.729 0.466
L1.Salzburg 0.039537 0.091793 0.431 0.667
L1.Steiermark 0.116071 0.128375 0.904 0.366
L1.Tirol 0.219292 0.084669 2.590 0.010
L1.Vorarlberg 0.016903 0.082239 0.206 0.837
L1.Wien -0.159486 0.173980 -0.917 0.359
======================================================================================
Results for equation Steiermark
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.184039 0.126881 1.450 0.147
L1.Burgenland -0.033883 0.061553 -0.550 0.582
L1.Kärnten -0.012467 0.049688 -0.251 0.802
L1.Niederösterreich 0.176835 0.148643 1.190 0.234
L1.Oberösterreich 0.403444 0.123570 3.265 0.001
L1.Salzburg -0.027339 0.063650 -0.430 0.668
L1.Steiermark -0.045661 0.089017 -0.513 0.608
L1.Tirol 0.188114 0.058710 3.204 0.001
L1.Vorarlberg 0.034758 0.057025 0.610 0.542
L1.Wien 0.142676 0.120640 1.183 0.237
======================================================================================
Results for equation Tirol
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.205190 0.160700 1.277 0.202
L1.Burgenland 0.079505 0.077959 1.020 0.308
L1.Kärnten -0.043885 0.062932 -0.697 0.486
L1.Niederösterreich -0.016035 0.188262 -0.085 0.932
L1.Oberösterreich -0.125467 0.156506 -0.802 0.423
L1.Salzburg 0.009645 0.080615 0.120 0.905
L1.Steiermark 0.382227 0.112743 3.390 0.001
L1.Tirol 0.520946 0.074359 7.006 0.000
L1.Vorarlberg 0.221411 0.072225 3.066 0.002
L1.Wien -0.231761 0.152794 -1.517 0.129
======================================================================================
Results for equation Vorarlberg
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.093841 0.184900 0.508 0.612
L1.Burgenland 0.030791 0.089699 0.343 0.731
L1.Kärnten -0.114796 0.072409 -1.585 0.113
L1.Niederösterreich 0.188203 0.216613 0.869 0.385
L1.Oberösterreich 0.018764 0.180075 0.104 0.917
L1.Salzburg 0.224246 0.092756 2.418 0.016
L1.Steiermark 0.149900 0.129721 1.156 0.248
L1.Tirol 0.087829 0.085557 1.027 0.305
L1.Vorarlberg 0.038456 0.083101 0.463 0.644
L1.Wien 0.293263 0.175804 1.668 0.095
======================================================================================
Results for equation Wien
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.589497 0.102712 5.739 0.000
L1.Burgenland -0.016107 0.049828 -0.323 0.747
L1.Kärnten 0.000410 0.040223 0.010 0.992
L1.Niederösterreich -0.014967 0.120328 -0.124 0.901
L1.Oberösterreich 0.278077 0.100031 2.780 0.005
L1.Salzburg 0.006486 0.051526 0.126 0.900
L1.Steiermark 0.006237 0.072060 0.087 0.931
L1.Tirol 0.076275 0.047527 1.605 0.109
L1.Vorarlberg 0.179432 0.046163 3.887 0.000
L1.Wien -0.099320 0.097659 -1.017 0.309
======================================================================================
Correlation matrix of residuals
Burgenland Kärnten Niederösterreich Oberösterreich Salzburg Steiermark Tirol Vorarlberg Wien
Burgenland 1.000000 0.132151 -0.019553 0.188362 0.242477 0.032878 0.089254 -0.124829 0.147503
Kärnten 0.132151 1.000000 -0.035181 0.172863 0.121202 -0.165795 0.168191 0.020451 0.292181
Niederösterreich -0.019553 -0.035181 1.000000 0.237943 0.055045 0.185603 0.079209 0.020797 0.344142
Oberösterreich 0.188362 0.172863 0.237943 1.000000 0.265653 0.268938 0.078333 0.049272 0.058523
Salzburg 0.242477 0.121202 0.055045 0.265653 1.000000 0.136845 0.064014 0.070120 -0.042489
Steiermark 0.032878 -0.165795 0.185603 0.268938 0.136845 1.000000 0.090862 0.063616 -0.171655
Tirol 0.089254 0.168191 0.079209 0.078333 0.064014 0.090862 1.000000 0.127609 0.119160
Vorarlberg -0.124829 0.020451 0.020797 0.049272 0.070120 0.063616 0.127609 1.000000 0.072971
Wien 0.147503 0.292181 0.344142 0.058523 -0.042489 -0.171655 0.119160 0.072971 1.000000